# Deep, spatially coherent Inverse Sensor Models with Uncertainty Incorporation using the evidential Framework

## TL;DR

This paper introduces an evidential neural network framework for radar-based environment perception in autonomous cars, enabling dense, spatially coherent, and uncertainty-aware inference from sparse, noisy radar data, thus improving perception speed.

## Contribution

It extends evidential convolutional neural networks to incorporate sensor and model uncertainty for inverse sensor models, enhancing environment perception in autonomous vehicles.

## Key findings

- Denser environment perception achieved in fewer time steps.
- Effective incorporation of sensor noise and model uncertainty.
- Improved spatial coherence in environment inference.

## Abstract

To perform high speed tasks, sensors of autonomous cars have to provide as much information in as few time steps as possible. However, radars, one of the sensor modalities autonomous cars heavily rely on, often only provide sparse, noisy detections. These have to be accumulated over time to reach a high enough confidence about the static parts of the environment. For radars, the state is typically estimated by accumulating inverse detection models (IDMs). We employ the recently proposed evidential convolutional neural networks which, in contrast to IDMs, compute dense, spatially coherent inference of the environment state. Moreover, these networks are able to incorporate sensor noise in a principled way which we further extend to also incorporate model uncertainty. We present experimental results that show This makes it possible to obtain a denser environment perception in fewer time steps.